Self-driving control logic based on semantic segmentation
This is a great question. Connecting the AI knowledge to the application is a difficult task and its not easy to test and perfect.

There are a few points regarding autonomous driving algorithms that one should keep in mind.


A lot of data is required before actually applying a model to the real world.
The control algorithms are designed for specific tasks, there is no master algorithm that solves the issue.
Time is very important, decisions have to be made in real time.


The image segmentation you have mentioned in the question is rarely used in current autonomous technologies. The main issue is that 


The model is slow and requires massive computing resources. Due to this is cannot help in real time decision making.
A very important parameter depth is missing from the information. This is of utmost importance in autonomous driving technology. A 2D image cannot provide this parameter.


So the current technology use 


A range of sensors (LIDAR, Ultrasonic, Cameras etc.) that provide readings 100+ times a second.
A lot of the computer vision algorithms and control algorithms are hard coded. There are set parameters within which decisions are made.
The control algorithms are designed to work with each set of sensors and the mechanical components of the car.


To get a better look at the way these algorithms are used, you can have a look at 


Stanley vision system winner of the DARPA grand challenge (Stanford)
How a driverless car sees the road TEDx